1. Staircase Plot

A staircase graph using matplotlib

import matplotlib.pyplot as plt
import numpy as np

plt.style.use('_mpl-gallery')

y = [7.5, 5.7, 3.5, 2.8, 3.9, 6.6, 2.9, 8.5, 3.7]


fig, ax = plt.subplots()

ax.stairs(y, linewidth=2.5)

ax.set(xlim=(0, 10), xticks=np.arange(1, 10),
       ylim=(0, 10), yticks=np.arange(1, 10))

plt.show()

2. Gridded Plot

Contour(X, Y, Z) graph using matplotlib

import matplotlib.pyplot as plt
import numpy as np

plt.style.use('_mpl-gallery-nogrid')

# make data
X, Y = np.meshgrid(np.linspace(-3, 3, 256), np.linspace(-3, 3, 256))
Z = (1 - X/4 + X**3 + Y**2) * np.exp(-X**2 - Y**2)
levels = np.linspace(np.min(Z), np.max(Z), 7)

# plot
fig, ax = plt.subplots()

ax.contour(X, Y, Z, levels=levels)

plt.show()

3. Bubble Chart

Bubble chart using Plotly

import plotly
plotly.offline.init_notebook_mode()
import plotly.express as px
df = px.data.gapminder()

fig = px.scatter(df.query("year==2007"), x="gdpPercap", y="lifeExp",
	         size="pop", color="continent",
                 hover_name="country", log_x=True, size_max=40)
fig.show()

4. Displot

Conditional kernel density estimate using Seaborn

import seaborn as sns
sns.set_theme(style="whitegrid")

# Load the diamonds dataset
diamonds = sns.load_dataset("diamonds")

# Plot the distribution of clarity ratings, conditional on carat
sns.displot(
    data=diamonds,
    x="carat", hue="cut",
    kind="kde", height=4,
    multiple="fill", clip=(0, None),
    palette="ch:rot=-.25,hue=1,light=.75",
)

<seaborn.axisgrid.FacetGrid at 0x2c21722ffd0>